class_docstr: Randomly select an elem of the dataset (to have smaller dataset) with a bernouilli distribution of parameter thresh.
  
  Parameters
  ----------
  thresh: float, optional
      the parameter of the bernouilli function
class_docstr_tok: ["Randomly","select","an","elem","of","the","dataset","(","to","have","smaller","dataset",")","with","a","bernouilli","distribution","of","parameter","thresh",".","Parameters","--","--","--","--","--","thresh",":","float",",","optional","the","parameter","of","the","bernouilli","function"]
code: def __call__(self, data):
      return random.random() < self.thresh
  
file_path: torch-points3d/torch_points3d/core/data_transform/filters.py
imports: import numpy
  import torch
  import random
  from torch_points3d.core.data_transform.features import PCACompute, compute_planarity
label_desc: Use image node_id 2 for calling the RandomFilter obj's underlying member method code with example usage: obj.__call__(data) and returns: unknown